The present invention relates to estimating a state of at least one target using a plurality of sensors.
The deployment of sensor networks to monitor and track events, objects and situations in a variety of environments is becoming widespread. Uncertainty due to random noise is endemic in sensor measurements, but it is known to attempt to reduce its effect using filtering processes. Sensors can generate faulty measurements for a number of reasons, such as power failure, physical damage, and mis-calibration. If parameterised models for the fault types are available, they can be exploited by a fault recognition algorithm; however, often an explicit fault model is not known, in which case the algorithm must be able to deal with model incompleteness.
It is possible to decentralise the filtering processes in order to try to reduce the computational and communication bottlenecks; however, this can exacerbate the problem of a faulty sensor because its measurement may be fused at multiple sites and then the fused data is re-propagated to further fusion nodes so the impact of a fault can spread through the network. This means that there is a particularly strong imperative to deal with faulty sensor data in a networked sensor fusion system.